{"id":"W4386311025","doi":"10.2139/ssrn.4552752","title":"Rapid Identification of Salmonella Serovars Enteritidis and Typhimurium Using Whole Cell Matrix Assisted Laser Desorption Ionization – Time of Flight Mass Spectrometry (MALDI-TOF MS) Coupled with Multivariate Analysis and Artificial Intelligence","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Salmonella enteritidis; Mass spectrometry; Chromatography; Salmonella; Matrix-assisted laser desorption/ionization; Matrix (chemical analysis); Chemistry; Analytical Chemistry (journal); Time-of-flight mass spectrometry; Identification (biology); Multivariate statistics; Ionization; Desorption; Biology; Computer science; Bacteria; Adsorption","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00157503,0.0002392628,0.0004136666,0.0004297777,0.0001165825,0.0001215875,0.0001832829,0.0002646295,0.0000169441],"category_scores_gemma":[0.0001256527,0.0002411937,0.000137452,0.0005648122,0.0001148915,0.00001971095,0.0001314966,0.0004817238,0.000002163012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001404522,"about_ca_system_score_gemma":0.0004297699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001444119,"about_ca_topic_score_gemma":0.000232907,"domain_scores_codex":[0.9976588,0.0002328901,0.0008681408,0.0005310793,0.000230611,0.0004785101],"domain_scores_gemma":[0.9979897,0.00003097718,0.00112282,0.0003456393,0.000444008,0.00006682322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002255222,0.0000916782,0.002834719,0.0001202323,0.0005524356,7.319101e-7,0.00007834996,0.002254127,0.9933109,0.00005165495,7.898127e-7,0.000478917],"study_design_scores_gemma":[0.0006138927,0.0005135778,0.04402611,0.0001811995,0.002077469,0.00008004028,0.001018213,0.1061571,0.8390692,0.005600569,0.000003812242,0.0006588091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7871192,0.0004155109,0.212064,0.00004395553,0.00008448306,0.0002053271,0.00005374137,0.00001160972,0.000002209659],"genre_scores_gemma":[0.9952051,0.001427763,0.002407287,0.000002618513,0.000107622,0.00000479287,0.0005680753,0.00003705458,0.0002396664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2096567,"threshold_uncertainty_score":0.9835595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01728966705916707,"score_gpt":0.2668998754181003,"score_spread":0.2496102083589332,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}